To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical o...To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.展开更多
A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM2.5 were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutan...A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM2.5 were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM2.5 concentration and weather conditions. Southern sites（DX, YDM and DS） experienced heavier pollution than northern ones（DL, CP and WL）. Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM2.5.Mutual information values of Air quality–Traffic–Meteorology（ATM–MI） revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO2.展开更多
基金supported by the National Natural Science Foundation of China (Nos.61503082 and 61403121)the Natural Science Foundation of Fujian Province (No. 2016J05145)+3 种基金the Fundamental Research Funds for the Central Universities (No. 2015B20214)the Program for New Century Excellent Talents in Fujian Province University (No. GY-Z18155)the Program for Outstanding Young Scientific Researcher in Fujian Province University (No. GY-Z160149)the Scientific Research Foundation of Fujian University of Technology (No. GY-Z17162).
文摘To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.
基金conducted as part of the project "Concentration prediction of urban air pollutants based on deep learning" funded by Doctoral scholarship program of Tsinghua Universitypartly financial support is also provided by the National Natural Science Foundation of China (Nos. 61304199 41471333)
文摘A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3,2016. The mean daily AQI and PM2.5 were 240.44 and 203.6 μg/m^3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM2.5 concentration and weather conditions. Southern sites（DX, YDM and DS） experienced heavier pollution than northern ones（DL, CP and WL）. Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM2.5.Mutual information values of Air quality–Traffic–Meteorology（ATM–MI） revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO2.
